We are submitting Integrated Multiscale Networks in Schizophrenia in response to RFA-MH-16-300. Schizophrenia (SCZ) is a generally devastating neuropsychiatric illness with considerable morbidity, mortality, and personal and societal cost. Genetic factors have been strongly implicated via family and twin data, and more recently directly through genome-wide association studies (GWAS) and sequencing studies. The primary objective of our project is to develop and apply advanced integrative methods for computational and functional analysis of networks, including but not limited to Bayesian network reconstruction and prediction algorithms of variant causality to identify key drivers of SCZ pathology for potential therapeutic intervention. To achieve this in Aim 1 we will construct single tissue and multi- tissue probabilistic causal network by applying a novel top-down and bottom-up or hypothesis-driven probabilistic causal network approaches in RNA sequencing key drivers of networks, novel pathways, and new mechanisms in SCZ pathology data from the CommonMind consortium, incorporating prior information. In Aim 2 we will use network models derived in Aim 1 in order to improve the predictive SCZ networks that could be used to identify SCZ-relevant transcription-based features that can be useful in therapeutic screening. Finally, in Aim 3 we will use modified RNA and cellular models to validate the network models, key drivers and investigate their phenotype effects.